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弱相依过程的经验似然方法AI 解读

This paper studies the method of empirical likelihood in models with weakly dependent processes. In such cases, if the likelihood function is formulated as if the data process were independent, obviously empirical likelihood fails. We propose to use empirical likelihood of blocks of observations to solve this problem in a nonparametric manner. This method of "blockwise empirical likelihood" preserves the dependence of data, and the resulting likelihood ratios can be used to…

回归变量与工具变量可能非平稳时的完全修正 IV、GIVE 与 GMM 估计AI 解读

This paper develops a general theory of instrumental variables (IV) estimation that allows for both I(1) and I(0) regressors and instruments. The main goal of this paper is to develop a theory in which one does not need to know the integration properties of the regressors in order to obtain efficient estimators. The estimation techniques involve an extension of the fully modified (FM) regression procedure that was introduced in earlier work by Phillips and Hansen (1990). FM…

非平稳回归中的有效工具变量估计AI 解读

A limit theory for instrumental variables (IV) estimation that allows for possibly nonstationary processes was developed in Kitamura and Phillips (1992, Fully Modified IV, GIVE, and GMM Estimation with Possibly Non-stationary Regressors and Instruments, mimeo, Yale University). This theory covers a case that is important for practitioners, where the nonstationarity of the regressors may not be of full rank, and shows that the fully modified (FM) regression procedure of…

含 I(2) 过程的协整系统估计AI 解读

This paper considers the properties of systems likelihood procedures for cointegrated systems when the I(2) variables are present. Two alternative methods are proposed: one is based on the full system likelihood, whereas another is based on the subsystem likelihood. By eliminating all unit roots in the system by the use of prior information concerning the presence of unit roots, these procedures yield estimates whose asymptotic distributions are mixed normal, free from…